Informatics Tools for High-throughput Analysis of Cancer Mutations

描述(由申请人提供)：大型肿瘤外显子组测序项目已经确定了大量的突变，其与癌症的相关性尚不清楚。为了开始满足这一需求，我们的团队开发了两个用于癌症突变高通量计算分析的网络应用程序：癌症相关变异分析工具箱(CRAVAT)和突变位置成像工具箱(MuPIT)。Cravat在一次批量上传中接受数百万个突变，并将突变从基因组坐标映射到带注释的转录本和蛋白质。MuPIT目前接受最多2500个SNV的批量上传，并从基因组坐标映射到蛋白质数据库(PDB)的蛋白质X射线晶体结构。我们建议将Cravat和MuPIT合并并强化为一个单一的Web应用程序，在该应用程序中，我们将大幅改进工具、用户界面、软件基础设施、与外部数据资源和社区使用的工具的集成，以及对受保护数据的支持。网络应用程序中所有工具的范围将扩大，以处理对癌症外显子中发现的所有小规模突变后果类型的分析。使用我们团队开发的分类器，Cravat分析确定了最有可能对蛋白质功能产生有害影响的突变，以及那些最有可能赋予癌细胞(驱动因素)选择优势的突变。分类器分数由注释补充，包括群体等位基因频率、以前在肿瘤组织类型中的出现情况以及基因功能类别，从而实现过滤(例如，消除多态)和优先排序。还提供了通过聚合来自队列中的突变的分类器分数来进行基因级别的注释和评分。MuPIT将突变从基因组位置映射到蛋白质结构，并提供对蛋白质结构上下文中的突变的交互查看，以及与 各种注解。为了能够对感兴趣的突变和基因进行优先排序，该应用程序提供了一个预览，描述每个结构和所有可用的注释(例如，结合位点、实验性突变结果、多态和疾病相关的变种，这些都已被记录在案)。在选择感兴趣的PDB之后，用户被引导到交互式可视化页面。增强的JMOL小程序显示映射到结构上的所有SNV。通常，输入列表中的许多SNV可以映射到单个结构上，揭示了关键功能站点周围的集群模式。仅基于口碑，自2012年8月这两个应用程序首次亮相以来，Cravat已经被来自39个国家的129名独立用户使用，它已经分析了1136个提交的工作岗位，总计2790万个突变。来自25个国家的242个独立用户使用了MuPIT，提交了720个工作。(来源：谷歌分析)。

DESCRIPTION (provided by applicant): Large tumor exome sequencing projects have identified a very large number of mutations whose cancer relevance is not yet understood. To begin to address this need, our team has produced two web applications for high-throughput computational analysis of cancer mutations: the Cancer-Related Analysis of VAriants Toolkit (CRAVAT) and the Mutation Position Imaging Toolbox (MuPIT). CRAVAT accepts millions of mutations in a single batch upload and maps mutations from genomic coordinates to annotated transcripts and proteins. MuPIT currently accepts batch uploads of up to 2500 SNVs and maps from genomic coordinates onto X-ray crystal structures of proteins from Protein Data Bank (PDB). We propose to combine and harden CRAVAT and MuPIT into a single web application, in which we will substantially improve the tools, user interface, software infrastructure, integration with external data resources and tools used by the community, and support for protected data. The scope of all tools in the web application will be broadened to handle analysis of the full range of small-scale mutation consequence types found in cancer exomes. CRAVAT analysis identifies mutations most likely to have deleterious impact on protein function and those that are most likely to confer a selective advantage to cancer cells (drivers), using classifiers developed by our team. Classifier scores are supplemented with annotations, including population allele frequencies, previous occurrence in tumor tissue types, and gene functional categories, enabling filtering (e.g. removing polymorphisms) and prioritization. Gene-level annotation and scoring, by aggregation of classifier scores from mutations in a cohort is also provided. MuPIT maps mutations from genomic positions onto to protein structures and provides interactive viewing of mutations in the context of protein structure, and in relation to a variety of annotations. To enable prioritization of interesting mutations and genes, the application provides a preview describing each structure and all available annotations (e.g., binding sites, experimental mutagenesis results, polymorphic and disease- associated variants that have been previously documented). After selecting a PDB of interest, the user is led to an interactive visualization page. An enhanced Jmol applet displays all SNVs mapped onto the structure. Frequently, many SNVs in the input list can be mapped onto a single structure, revealing clustering patterns around key functional sites. Based only on word-of-mouth, since the debut of the two applications in August 2012, CRAVAT has been utilized by 129 unique users from 39 countries, and it has analyzed 1,136 submitted jobs, totaling 27.9 million mutations. MuPIT has been utilized by 242 unique users from 25 countries, with 720 submitted jobs. (Source: Google Analytics).